Abstract
Link prediction refers to estimating the possibility of the existence of non-existent links between the nodes. The link prediction algorithms based on local information merely consider nodes’ attributes or a small amount of topology information about common neighbors. In this paper, we proposed a new measure motivated by the cohesion between common neighbors and the predicted nodes——LNL (Local Neighbors Link). Experiments show that, compared with four classical algorithms on seven real networks, LNL has the higher accuracy and robustness. Furthermore, we apply the link prediction algorithms into large-scale networks. We implement the LNL method in both MapReduce and Spark, the experiments show that the implementation by Spark has higher efficiency than using MapReduce.
This work is supported in part by the National Key Basic Research and Department (973) Program of China (No. 2013CB329606), and the National Natural Science Foundation of China (No. 71231002, 61375058).
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Yang, J., Yang, L., Zhang, P. (2015). A New Link Prediction Algorithm Based on Local Links. In: Xiao, X., Zhang, Z. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9391. Springer, Cham. https://doi.org/10.1007/978-3-319-23531-8_2
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